A Novel Probability Binary Particle Swarm Optimization Algorithm and Its Application

Abstract

<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 8.5pt;">Particle swarm optimization (PSO), an intelligent optimization algorithm inspired by the flocking behavior of birds, has been shown to perform well and widely used to solve the continuous problem. But the traditional PSO and most of its variants are developed for optimization problems in continuous space, which are not able to solve the binary combinational optimization problem. To tackle this problem, Kennedy extended the PSO and proposed a discrete binary PSO. But its performance is not ideal and just few further works were conducted based on it. In this paper, we propose a novel probability binary particle swarm optimization (PBPSO) algorithm for discrete binary optimization problems. In PBPSO, a novel updating strategy is adopted to update the swarm and search the global solution, which further simplify the computations and improve the optimization ability. To investigate the performance of the proposed PBPSO, the multidimensional knapsack problems are used as the test benchmarks. The experimental results demonstrate that PBPSO has a better performance for solving the multidimensional knapsack problem in terms of convergent speed and global search ability.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 8.5pt;"></span></p

Set-based particle swarm optimization applied to the multidimensional knapsack problem

“…The fraction of particles that is re-initialized at each iteration is denoted by p reset . Wang et al (2008) proposed a variant of BPSO called the probability binary PSO (PBPSO) and applied this to the MKP. The velocity update equation of PBPSO is the same as for continuous PSO given in equation (2).…”

“…Labed et al (2011) proposed a hybrid GA binary PSO algorithm that includes a crossover operator and a separate repair operator that modifies positions to represent feasible solutions to the MKP. Wang et al (2008) used the MKP to compare the binary PSO to two other discrete PSO variants, namely MBPSO and PBPSO. Recent studies into the MKP frequently use the benchmark problems mentioned in Chu and Beasley (1998) to compare the performance of algorithms.…”

A review of intelligent approaches for designing dynamic and robust layouts in flexible manufacturing systems

“…Instead of using the gbest value, the local best (lbest) value, which has been found so far by any particle in its neighbouring area, can also be considered. According to Rezazadeh et al [134] and Wang et al [159], in a PSO including m particles, the position and velocity of the particle i at iteration t are updated by using Eqs. 5 and 6, respectively.…”

A review on implementation of meta-heuristic approaches for layout problems in dynamic business environment

“…Asl and Wong (2015) Solved unequal-area static and DFLPs by implementing modified PSO. According to Wang et al (2008c) and Rezazadeh et al (2009), in PSO, including m particles, equation (5) and equation (6) are used to update the position and velocity of the particle i at iteration t respectively. The dimensional vectors 1 2 3 ( , , , , ) 2, 3,…,m) represents the position and flying velocity coordinates of the i th particle at iteration t. The position coordinates of particle i associated with its pbest and gbest fitness values at iteration t are also represented by…”